| Issue |
E3S Web of Conf.
Volume 222, 2020
International Scientific and Practical Conference “Development of the Agro-lndustrial Complex in the Context of Robotization and Digitalization of Production in Russia and Abroad” (DAIC 2020)
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|---|---|---|
| Article Number | 05023 | |
| Number of page(s) | 6 | |
| Section | Modern Problems of Interaction Between Man and Nature, Man and Technology, and Social Institutions. Adequate Response of Science and Society to Global Challenges in the Context of Production Intellectualization | |
| DOI | https://doi.org/10.1051/e3sconf/202022205023 | |
| Published online | 22 December 2020 | |
Identification of the features of the regions that are most preferable for the use of precision farming technologies in agricultural production
1 Ural State Agrarian University, Department of Land Management, 620075 Russia, Ekaterinburg, Russia
2 Ural State Economic University Ekaterinburg, Department of Competition Law and Antimonopoly Regulation, 620219 Ekaterinburg, Russia,
* Corresponding author: alexey.anser@gmail.com
As the main hypothesis, it is suggested that the existing unevenness in the number of precision farming elements used in agriculture in the subjects of the Russian Federation is related to regional characteristics and specific features of the agricultural sector of the regional economy. The purpose of the study is to identify the geographical features of the regions that are most preferable for the use of precision farming technology in agricultural production. Mathematical modeling uses data from 20 subjects of the Russian Federation on the dynamics of the introduction and use of precision farming elements and the characteristics of these regions for 14 different indicators that can in one way or another affect the introduction of these technologies. Multiple correlation was obtained using 5 characteristics of regions (the correlation coefficient was r=0.89±0.1). At the same time, two indicators (the change in the level of registered unemployment and the amount of subsidies per 1 ha of agricultural land) were inversely dependent on the result of the introduction of precision farming elements. The selected indicators determined the intensity of introduction of precision farming elements in the regions by almost 80% (the coefficient of determination was D=0.798). The identification of these features and the construction of an appropriate model allows to predict the most preferred regions for the use of precision farming elements in agricultural production based on the generalization of existing experience.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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